Multidimensional shannon entropy (HM) as an approach to classify H&E colorectal images

被引：0

作者：

Segato dos Santos, Luiz Fernando ^{[1
]}

Rozendo, Guilherme Botazzo ^{[1
]}

do Nascimento, Marcelo Zanchetta ^{[2
]}

Azevedo Tosta, Thaina Aparecida ^{[3
]}

da Costa Longo, Leonardo Henrique ^{[1
]}

Neves, Leandro Alves ^{[1
]}

机构：

[1] Sao Paulo State Univ, Dept Comp Sci & Stat DCCE, Sao Jose Do Rio Preto, Brazil

[2] Fed Univ Uberrandia UFU, Fac Comp Sci FACOM, Uberlandia, MG, Brazil

[3] Fed Univ Sao Paulo UNIFESP, Sci & Technol Inst, Sao Jose Dos Campos, Brazil

来源：

2022 29TH INTERNATIONAL CONFERENCE ON SYSTEMS, SIGNALS AND IMAGE PROCESSING (IWSSIP) | 2022年

关键词：

shannon entropy; multiscale; multidimensional; combination; colorectal images; FRACTAL DIMENSION;

D O I：

10.1109/IWSSIP55020.2022.9854438

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this work, we have proposed a method that combines multiscale and multidimensional approaches with Shannon entropy, named H-M. The method was combined with other fractal and sample entropy techniques and tested on H&E colorectal images. The results provided an accuracy of 95.36% for the combination H-M and SampEn(MF). The combinations and analyses presented here are important contributions to the Literature focused on the investigation of techniques for the development of computer-aided diagnosis.

引用

页数：4

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